Recensement

Dataviz sur les données du recensement 2019 avec recoupement sur les logements

!pip install pandas seaborn dataprep
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Requirement already satisfied: seaborn in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (0.11.2)
Requirement already satisfied: dataprep in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (0.4.3)
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Requirement already satisfied: soupsieve>1.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.3.2.post1)
Requirement already satisfied: webencodings in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.5.1)
Requirement already satisfied: pycparser in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.21)
WARNING: There was an error checking the latest version of pip.

!wget "https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_individus.csv
--2022-05-24 05:26:13--  https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.55.171.246, 13.211.119.48
Connecting to data.gouv.nc (data.gouv.nc)|13.55.171.246|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_individus.csv’


          data/rece     [<=>                 ]       0  --.-KB/s               
         data/recen     [ <=>                ]  36.68K   179KB/s               
        data/recens     [  <=>               ] 100.68K   245KB/s               
       data/recense     [   <=>              ] 132.67K   188KB/s               
      data/recensem     [    <=>             ] 180.66K   185KB/s               
     data/recenseme     [     <=>            ] 244.65K   195KB/s               
    data/recensemen     [      <=>           ] 260.64K   171KB/s               
   data/recensement     [       <=>          ] 308.64K   169KB/s               
  data/recensement_     [        <=>         ] 372.62K   177KB/s               
 data/recensement_i     [         <=>        ] 404.61K   166KB/s               
data/recensement_in     [          <=>       ] 436.60K   159KB/s               
ata/recensement_ind     [           <=>      ] 500.59K   164KB/s               
ta/recensement_indi     [            <=>     ] 532.58K   162KB/s               
a/recensement_indiv     [             <=>    ] 596.57K   163KB/s               
/recensement_indivi     [              <=>   ] 676.55K   155KB/s               
recensement_individ     [               <=>  ] 756.53K   165KB/s               
ecensement_individu     [                <=> ] 764.55K   155KB/s               
censement_individus     [                 <=>] 796.55K   155KB/s               
ensement_individus.     [                <=> ] 860.55K   156KB/s               
nsement_individus.c     [               <=>  ] 876.55K   152KB/s               
sement_individus.cs     [              <=>   ] 924.55K   152KB/s               
ement_individus.csv     [             <=>    ] 988.55K   150KB/s               
ment_individus.csv      [            <=>     ]   1005K   148KB/s               
ent_individus.csv       [           <=>      ]   1.03M   147KB/s               
nt_individus.csv        [          <=>       ]   1.09M   147KB/s               
t_individus.csv         [         <=>        ]   1.11M   146KB/s               
_individus.csv          [        <=>         ]   1.15M   146KB/s               
individus.csv           [       <=>          ]   1.21M   147KB/s               
ndividus.csv            [      <=>           ]   1.23M   143KB/s               
dividus.csv             [     <=>            ]   1.34M   150KB/s               
ividus.csv              [    <=>             ]   1.43M   149KB/s               
vidus.csv               [   <=>              ]   1.46M   147KB/s               
idus.csv                [  <=>               ]   1.50M   144KB/s               
dus.csv                 [ <=>                ]   1.56M   153KB/s               
us.csv                  [<=>                 ]   1.59M   147KB/s               
s.csv                   [ <=>                ]   1.62M   145KB/s               
.csv                    [  <=>               ]   1.68M   148KB/s               
csv                     [   <=>              ]   1.70M   143KB/s               
sv                      [    <=>             ]   1.75M   145KB/s               
v                       [     <=>            ]   1.82M   141KB/s               
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                  d     [       <=>          ]   1.96M   143KB/s               
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                dat     [         <=>        ]   2.06M   146KB/s               
               data     [          <=>       ]   2.09M   139KB/s               
              data/     [           <=>      ]   2.15M   149KB/s               
             data/r     [            <=>     ]   2.17M   140KB/s               
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          data/rece     [               <=>  ]   2.29M   140KB/s               
         data/recen     [                <=> ]   2.34M   143KB/s               
        data/recens     [                 <=>]   2.40M   142KB/s               
       data/recense     [                <=> ]   2.43M   142KB/s               
      data/recensem     [               <=>  ]   2.46M   145KB/s               
     data/recenseme     [              <=>   ]   2.53M   142KB/s               
    data/recensemen     [             <=>    ]   2.54M   142KB/s               
   data/recensement     [            <=>     ]   2.59M   143KB/s               
  data/recensement_     [           <=>      ]   2.65M   143KB/s               
 data/recensement_i     [          <=>       ]   2.68M   145KB/s               
data/recensement_in     [         <=>        ]   2.71M   142KB/s               
ata/recensement_ind     [        <=>         ]   2.78M   143KB/s               
ta/recensement_indi     [       <=>          ]   2.81M   143KB/s               
a/recensement_indiv     [      <=>           ]   2.87M   148KB/s               
/recensement_indivi     [     <=>            ]   2.89M   144KB/s               
recensement_individ     [    <=>             ]   2.93M   145KB/s               
ecensement_individu     [   <=>              ]   3.00M   146KB/s               
censement_individus     [  <=>               ]   3.01M   144KB/s               
ensement_individus.     [ <=>                ]   3.06M   144KB/s               
nsement_individus.c     [<=>                 ]   3.12M   143KB/s               
sement_individus.cs     [ <=>                ]   3.14M   143KB/s               
ement_individus.csv     [  <=>               ]   3.18M   140KB/s               
ment_individus.csv      [   <=>              ]   3.25M   142KB/s               
ent_individus.csv       [    <=>             ]   3.26M   138KB/s               
nt_individus.csv        [     <=>            ]   3.31M   141KB/s               
t_individus.csv         [      <=>           ]   3.37M   143KB/s               
_individus.csv          [       <=>          ]   3.40M   145KB/s               
individus.csv           [        <=>         ]   3.43M   137KB/s               
ndividus.csv            [         <=>        ]   3.50M   136KB/s               
dividus.csv             [          <=>       ]   3.53M   135KB/s               
ividus.csv              [           <=>      ]   3.59M   138KB/s               
vidus.csv               [            <=>     ]   3.62M   135KB/s               
idus.csv                [             <=>    ]   3.71M   131KB/s               
dus.csv                 [              <=>   ]   3.73M   124KB/s               
us.csv                  [               <=>  ]   3.78M   124KB/s               
s.csv                   [                <=> ]   3.83M   128KB/s               
.csv                    [                 <=>]   3.87M   123KB/s               
csv                     [                <=> ]   3.90M   126KB/s               
sv                      [               <=>  ]   3.96M   128KB/s               
v                       [              <=>   ]   4.00M   124KB/s               
                        [             <=>    ]   4.03M   127KB/s               
                  d     [            <=>     ]   4.09M   132KB/s               
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                dat     [          <=>       ]   4.15M   130KB/s               
               data     [         <=>        ]   4.21M   128KB/s               
              data/     [        <=>         ]   4.23M   123KB/s               
             data/r     [       <=>          ]   4.31M   130KB/s               
            data/re     [      <=>           ]   4.34M   136KB/s               
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          data/rece     [    <=>             ]   4.43M   136KB/s               
         data/recen     [   <=>              ]   4.46M   131KB/s               
        data/recens     [  <=>               ]   4.50M   131KB/s               
       data/recense     [ <=>                ]   4.56M   139KB/s               
      data/recensem     [<=>                 ]   4.59M   143KB/s               
     data/recenseme     [ <=>                ]   4.62M   146KB/s               
    data/recensemen     [  <=>               ]   4.68M   147KB/s               
   data/recensement     [   <=>              ]   4.71M   146KB/s               
  data/recensement_     [    <=>             ]   4.75M   147KB/s               
 data/recensement_i     [     <=>            ]   4.81M   146KB/s               
data/recensement_in     [      <=>           ]   4.84M   147KB/s               
ata/recensement_ind     [       <=>          ]   4.87M   145KB/s               
ta/recensement_indi     [        <=>         ]   4.93M   147KB/s               
a/recensement_indiv     [         <=>        ]   4.96M   146KB/s               
/recensement_indivi     [          <=>       ]   5.03M   150KB/s               
recensement_individ     [           <=>      ]   5.06M   148KB/s               
ecensement_individu     [            <=>     ]   5.15M   144KB/s               
censement_individus     [             <=>    ]   5.18M   145KB/s               
ensement_individus.     [              <=>   ]   5.21M   146KB/s               
nsement_individus.c     [               <=>  ]   5.28M   146KB/s               
sement_individus.cs     [                <=> ]   5.31M   148KB/s               
ement_individus.csv     [                 <=>]   5.34M   148KB/s               
ment_individus.csv      [                <=> ]   5.40M   147KB/s               
ent_individus.csv       [               <=>  ]   5.46M   146KB/s               
nt_individus.csv        [              <=>   ]   5.53M   153KB/s               
t_individus.csv         [             <=>    ]   5.56M   146KB/s               
_individus.csv          [            <=>     ]   5.59M   146KB/s               
individus.csv           [           <=>      ]   5.65M   150KB/s               
ndividus.csv            [          <=>       ]   5.68M   147KB/s               
dividus.csv             [         <=>        ]   5.75M   150KB/s               
ividus.csv              [        <=>         ]   5.78M   152KB/s               
vidus.csv               [       <=>          ]   5.81M   143KB/s               
idus.csv                [      <=>           ]   5.87M   152KB/s               
dus.csv                 [     <=>            ]   5.90M   145KB/s               
us.csv                  [    <=>             ]   6.00M   155KB/s               
s.csv                   [   <=>              ]   6.06M   159KB/s               
.csv                    [  <=>               ]   6.15M   166KB/s               
csv                     [ <=>                ]   6.25M   159KB/s               
sv                      [<=>                 ]   6.28M   155KB/s               
v                       [ <=>                ]   6.31M   155KB/s               
                        [  <=>               ]   6.37M   162KB/s               
                  d     [   <=>              ]   6.43M   164KB/s               
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                dat     [     <=>            ]   6.62M   177KB/s               
               data     [      <=>           ]   6.68M   183KB/s               
              data/     [       <=>          ]   6.78M   192KB/s               
             data/r     [        <=>         ]   6.87M   197KB/s               
            data/re     [         <=>        ]   6.96M   206KB/s               
           data/rec     [          <=>       ]   7.03M   206KB/s               
          data/rece     [           <=>      ]   7.12M   215KB/s               
         data/recen     [            <=>     ]   7.21M   227KB/s               
        data/recens     [             <=>    ]   7.28M   224KB/s               
       data/recense     [              <=>   ]   7.37M   235KB/s               
      data/recensem     [               <=>  ]   7.46M   240KB/s               
     data/recenseme     [                <=> ]   7.50M   234KB/s               
    data/recensemen     [                 <=>]   7.59M   241KB/s               
   data/recensement     [                <=> ]   7.65M   239KB/s               
  data/recensement_     [               <=>  ]   7.75M   267KB/s               
 data/recensement_i     [              <=>   ]   7.84M   269KB/s               
data/recensement_in     [             <=>    ]   7.90M   271KB/s               
ata/recensement_ind     [            <=>     ]   8.00M   272KB/s               
ta/recensement_indi     [           <=>      ]   8.09M   273KB/s               
a/recensement_indiv     [          <=>       ]   8.18M   272KB/s               
/recensement_indivi     [         <=>        ]   8.25M   273KB/s               
recensement_individ     [        <=>         ]   8.34M   272KB/s               
ecensement_individu     [       <=>          ]   8.43M   271KB/s               
censement_individus     [      <=>           ]   8.50M   264KB/s               
ensement_individus.     [     <=>            ]   8.59M   268KB/s               
nsement_individus.c     [    <=>             ]   8.68M   266KB/s               
sement_individus.cs     [   <=>              ]   8.75M   259KB/s               
ement_individus.csv     [  <=>               ]   8.84M   262KB/s               
ment_individus.csv      [ <=>                ]   8.93M   260KB/s               
ent_individus.csv       [<=>                 ]   9.03M   258KB/s               
nt_individus.csv        [ <=>                ]   9.09M   257KB/s               
t_individus.csv         [  <=>               ]   9.18M   259KB/s               
_individus.csv          [   <=>              ]   9.28M   259KB/s               
individus.csv           [    <=>             ]   9.34M   259KB/s               
ndividus.csv            [     <=>            ]   9.43M   259KB/s               
dividus.csv             [      <=>           ]   9.53M   265KB/s               
ividus.csv              [       <=>          ]   9.62M   270KB/s               
vidus.csv               [        <=>         ]   9.68M   265KB/s               
idus.csv                [         <=>        ]   9.78M   260KB/s               
dus.csv                 [          <=>       ]   9.87M   261KB/s               
us.csv                  [           <=>      ]   9.93M   261KB/s               
s.csv                   [            <=>     ]  10.03M   260KB/s               
.csv                    [             <=>    ]  10.12M   266KB/s               
csv                     [              <=>   ]  10.18M   262KB/s               
sv                      [               <=>  ]  10.28M   264KB/s               
v                       [                <=> ]  10.37M   269KB/s               
                        [                 <=>]  10.46M   272KB/s               
                  d     [                <=> ]  10.53M   269KB/s               
                 da     [               <=>  ]  10.62M   272KB/s               
                dat     [              <=>   ]  10.71M   278KB/s               
               data     [             <=>    ]  10.78M   272KB/s               
              data/     [            <=>     ]  10.87M   271KB/s               
             data/r     [           <=>      ]  10.96M   276KB/s               
            data/re     [          <=>       ]  11.06M   275KB/s               
           data/rec     [         <=>        ]  11.12M   269KB/s               
          data/rece     [        <=>         ]  11.21M   269KB/s               
         data/recen     [       <=>          ]  11.31M   275KB/s               
        data/recens     [      <=>           ]  11.37M   276KB/s               
       data/recense     [     <=>            ]  11.46M   276KB/s               
      data/recensem     [    <=>             ]  11.56M   275KB/s               
     data/recenseme     [   <=>              ]  11.62M   269KB/s               
    data/recensemen     [  <=>               ]  11.71M   274KB/s               
   data/recensement     [ <=>                ]  11.81M   274KB/s               
  data/recensement_     [<=>                 ]  11.87M   274KB/s               
 data/recensement_i     [ <=>                ]  11.96M   274KB/s               
data/recensement_in     [  <=>               ]  12.06M   276KB/s               
ata/recensement_ind     [   <=>              ]  12.12M   271KB/s               
ta/recensement_indi     [    <=>             ]  12.21M   275KB/s               
a/recensement_indiv     [     <=>            ]  12.31M   274KB/s               
/recensement_indivi     [      <=>           ]  12.37M   267KB/s               
recensement_individ     [       <=>          ]  12.46M   270KB/s               
ecensement_individu     [        <=>         ]  12.56M   266KB/s               
censement_individus     [         <=>        ]  12.65M   264KB/s               
ensement_individus.     [          <=>       ]  12.71M   258KB/s               
nsement_individus.c     [           <=>      ]  12.81M   260KB/s               
sement_individus.cs     [            <=>     ]  12.90M   258KB/s               
ement_individus.csv     [             <=>    ]  12.96M   251KB/s               
ment_individus.csv      [              <=>   ]  13.06M   255KB/s               
ent_individus.csv       [               <=>  ]  13.15M   255KB/s               
nt_individus.csv        [                <=> ]  13.25M   260KB/s               
t_individus.csv         [                 <=>]  13.31M   255KB/s               
_individus.csv          [                <=> ]  13.40M   255KB/s               
individus.csv           [               <=>  ]  13.50M   255KB/s               
ndividus.csv            [              <=>   ]  13.56M   255KB/s               
dividus.csv             [             <=>    ]  13.65M   255KB/s               
ividus.csv              [            <=>     ]  13.75M   255KB/s               
vidus.csv               [           <=>      ]  13.84M   260KB/s               
idus.csv                [          <=>       ]  13.90M   256KB/s               
dus.csv                 [         <=>        ]  14.00M   257KB/s               
us.csv                  [        <=>         ]  14.09M   264KB/s               
s.csv                   [       <=>          ]  14.15M   261KB/s               
.csv                    [      <=>           ]  14.25M   265KB/s               
csv                     [     <=>            ]  14.34M   266KB/s               
sv                      [    <=>             ]  14.40M   268KB/s               
v                       [   <=>              ]  14.50M   272KB/s               
                        [  <=>               ]  14.59M   273KB/s               
                  d     [ <=>                ]  14.68M   281KB/s               
                 da     [<=>                 ]  14.75M   276KB/s               
                dat     [ <=>                ]  14.84M   277KB/s               
               data     [  <=>               ]  14.93M   276KB/s               
              data/     [   <=>              ]  15.00M   282KB/s               
             data/r     [    <=>             ]  15.09M   281KB/s               
            data/re     [     <=>            ]  15.18M   280KB/s               
           data/rec     [      <=>           ]  15.25M   275KB/s               
          data/rece     [       <=>          ]  15.34M   271KB/s               
         data/recen     [        <=>         ]  15.43M   271KB/s               
        data/recens     [         <=>        ]  15.50M   271KB/s               
       data/recense     [          <=>       ]  15.59M   270KB/s               
      data/recensem     [           <=>      ]  15.68M   270KB/s               
     data/recenseme     [            <=>     ]  15.78M   273KB/s               
    data/recensemen     [             <=>    ]  15.84M   267KB/s               
   data/recensement     [              <=>   ]  15.93M   269KB/s               
  data/recensement_     [               <=>  ]  16.03M   274KB/s               
 data/recensement_i     [                <=> ]  16.09M   268KB/s               
data/recensement_in     [                 <=>]  16.18M   267KB/s               
ata/recensement_ind     [                <=> ]  16.28M   266KB/s               
ta/recensement_indi     [               <=>  ]  16.34M   263KB/s               
a/recensement_indiv     [              <=>   ]  16.43M   256KB/s               
/recensement_indivi     [             <=>    ]  16.53M   259KB/s               
recensement_individ     [            <=>     ]  16.59M   251KB/s               
ecensement_individu     [           <=>      ]  16.68M   247KB/s               
censement_individus     [          <=>       ]  16.78M   254KB/s               
ensement_individus.     [         <=>        ]  16.87M   251KB/s               
nsement_individus.c     [        <=>         ]  16.93M   248KB/s               
sement_individus.cs     [       <=>          ]  17.03M   252KB/s               
data/recensement_in     [      <=>           ]  17.14M   260KB/s    in 86s     

2022-05-24 05:27:41 (204 KB/s) - ‘data/recensement_individus.csv’ saved [17971968]
!wget "https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_logements.csv
--2022-05-24 05:27:41--  https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.55.171.246, 13.211.119.48
Connecting to data.gouv.nc (data.gouv.nc)|13.55.171.246|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_logements.csv’


          data/rece     [<=>                 ]       0  --.-KB/s               
         data/recen     [ <=>                ]  36.68K   183KB/s               
        data/recens     [  <=>               ] 100.67K   250KB/s               
       data/recense     [   <=>              ] 212.65K   352KB/s               
      data/recensem     [    <=>             ] 340.62K   395KB/s               
     data/recenseme     [     <=>            ] 356.61K   328KB/s               
    data/recensemen     [      <=>           ] 468.59K   313KB/s               
   data/recensement     [       <=>          ] 548.57K   292KB/s               
  data/recensement_     [        <=>         ] 700.55K   312KB/s               
 data/recensement_l     [         <=>        ] 796.55K   305KB/s               
data/recensement_lo     [          <=>       ] 892.55K   300KB/s               
ata/recensement_log     [           <=>      ] 988.55K   295KB/s               
ta/recensement_loge     [            <=>     ]   1.06M   292KB/s               
a/recensement_logem     [             <=>    ]   1.15M   289KB/s               
/recensement_logeme     [              <=>   ]   1.28M   294KB/s               
recensement_logemen     [               <=>  ]   1.37M   290KB/s               
ecensement_logement     [                <=> ]   1.46M   289KB/s               
censement_logements     [                 <=>]   1.56M   287KB/s               
ensement_logements.     [                <=> ]   1.65M   290KB/s               
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data/recensement_lo     [ <=>                ]  10.77M   272KB/s    in 59s     

2022-05-24 05:28:42 (187 KB/s) - ‘data/recensement_logements.csv’ saved [11293731]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data/recensement_individus.csv")
df['COUPLE'] = df['COUPLE'].astype("category")
df['COUPLE'] = df['COUPLE'].cat.rename_categories({1: 'Vit en couple', 2: 'ne vit pas en couple'})

df['CS24'] = df['CS24'].astype("category")
df['CS24'] = df['CS24'].cat.rename_categories({10: 'Agriculteurs exploitants', 21: 'Artisans', 22: 'Commerçants et assimilés', 23: 'Chefs d\'entreprise de 10 salariés ou plus',
                                               31: 'Professions libérales et assimilés', 32: 'Cadres de la fonction publique, professions intellectuelles et artistiques',
                                               36: 'Cadres d\'entreprise', 41: 'Professions intermédiaires de l\'enseignement, de la santé, de la fonction publique et assimilés',
                                               46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
                                               48: 'Contremaîtres, agents de maîtrise', 51: 'Employés de la fonction publique',
                                               54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce', 56: 'Personnels des services directs aux particuliers',
                                               61: 'Ouvriers qualifiés', 66: 'Ouvriers non qualifiés', 69: 'Ouvriers agricoles'})

df['CS42'] = df['CS42'].astype("category")
df['CS42'] = df['CS42'].cat.rename_categories({11: 'Agriculteurs sur petites exploitations', 12: 'Agriculteurs sur moyennes exploitations', 13: 'Agriculteurs sur grandes exploitations',
                                               21: 'Artisans', 22: 'Commerçants et assimilés' ,23: 'Chefs d\'entreprise de 10 salariés ou plus',
                                               31: 'Professions libérales et assimilés', 33: 'Cadres de la fonction publique', 34: 'Professeurs, professions scientifiques',
                                               35: 'Professions de l\'information, des arts et des spectacles', 37: 'Cadres administratifs et commerciaux d\'entreprise',
                                               38: 'Ingénieurs et cadres techniques d\'entreprise', 42: 'Professeurs des écoles, instituteurs et assimilés',
                                               43: 'Professions intermédiaires de la santé et du travail social',
                                               44: 'Clergé, religieux', 45: 'Professions intermédiaires administratives de la fonction publique',
                                               46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
                                               48: 'Contremaîtres, agents de maîtrise', 52: 'Employés civils et agents de service de la fonction publique',
                                               53: 'Policiers et militaires', 54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce',
                                               56: 'Personnels des services directs aux particuliers', 62: 'Ouvriers qualifiés de type industriel',
                                               63: 'Ouvriers qualifiés de type artisanal', 64: 'Chauffeurs', 65: 'Ouvriers qualifiés de la manutention, du magasinage et du transport',
                                               67: 'Ouvriers non qualifiés de type industriel', 68: 'Ouvriers non qualifiés de type artisanal',
                                               69: 'Ouvriers agricoles'})


df['CS8'] = df['CS8'].astype("category")
df['CS8'] = df['CS8'].cat.rename_categories({1: 'Agriculteurs exploitants', 2: 'Artisans, commerçants et chefs d\'entreprise',
                                             3: 'Cadres et professions intellectuelles supérieures', 4 : 'Professions Intermédiaires',
                                             5: 'Employés', 6: 'Ouvriers'})


df['CSSAL'] = df['CSSAL'].astype("category")
df['CSSAL'] = df['CSSAL'].cat.rename_categories({1: 'Manœuvre, ouvrier spécialisé', 2: 'Ouvrier qualifié ou hautement qualifié, technicien d’atelier',
                                                 3: 'Technicien (non cadre)', 4 : 'Agent de catégorie B de la fonction publique',
                                                 5: 'Agent de maîtrise, maîtrise administrative ou commerciale, VRP', 6: 'Agent de catégorie A de la fonction publique',
                                                 7: 'Ingénieur, cadre d’entreprise', 8: 'Agent de catégorie C ou D de la fonction publique',
                                                 9: 'Employé (par exemple : de bureau, de commerce, de la restauration, de maison)'})

EMPL_labels = {3: 'Artisan, commerçant, industriel, travailleur indépendant', 4: 'Stagiaire rémunéré, apprenti sous contrat',
                5: 'Salarié du secteur privé à durée déterminée', 6: 'Salarié du secteur privé à durée indéterminée',
                7 : 'Salarié du secteur public à durée déterminée', 8: 'Salarié du secteur public à durée indéterminé', }
df['EMPL'] = df['EMPL'].astype("category")


df['DIPL'] = df['DIPL'].astype("category")
diplomes_libelles = {1: 'Pas de scolarisation', 2: 'Aucun diplôme mais scolarisation jusqu’en primaire', 3: 'Aucun diplôme mais scolarisation jusqu’au collège', 
            4: 'Aucun diplôme mais scolarisation au-delà du collège', 11: 'CEP' , 12: 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 13: 'CAP, BEP ou diplôme de niveau équivalent',
            14: 'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
            15: 'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
            16: 'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
            17: 'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
            18: 'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
            19: 'Doctorat de recherche (hors santé)'}
#df['DIPL'] = df['DIPL'].cat.rename_categories(diplomes_libelles)




#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })

#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })


df.head()
ID IDLOG AGEA AGER ANNINS APE CNAT COUPLE CPAYSN CPAYSRA ... STAT STATANT STM TACT TP TRAANT TRANS TYP TYPEMPL TYPMENR
0 26769 76535.0 29 29 NaN NaN NaN Vit en couple NaN NaN ... 3.0 NaN 3 1.0 1.0 NaN 4.0 2 1.0 5.0
1 26772 43036.0 17 16 NaN NaN NaN ne vit pas en couple NaN NaN ... NaN NaN 6 3.0 NaN 2.0 5.0 2 NaN 5.0
2 26777 71698.0 19 19 NaN NaN NaN ne vit pas en couple NaN NaN ... NaN NaN 6 3.0 NaN 2.0 5.0 2 NaN 5.0
3 26784 43668.0 81 81 1965.0 NaN NaN Vit en couple NaN NaN ... NaN 2.0 1 5.0 NaN 1.0 2.0 2 NaN 2.0
4 26789 3381.0 36 35 2016.0 1610A NaN Vit en couple NaN NaN ... 3.0 NaN 3 1.0 1.0 NaN 4.0 2 1.0 5.0

5 rows × 43 columns

df.describe()
ID IDLOG AGEA AGER ANNINS CNAT CPAYSN CPAYSRA EXER GAD ... STAT STATANT STM TACT TP TRAANT TRANS TYP TYPEMPL TYPMENR
count 203144.000000 199929.000000 203144.000000 203144.000000 44082.000000 2412.000000 3137.00000 131.000000 89638.000000 203144.000000 ... 89155.000000 41032.000000 203144.000000 162368.000000 89638.000000 61332.000000 162852.000000 203144.000000 68942.000000 199844.000000
mean 135704.624434 54598.597797 35.574174 35.283617 1999.589651 432.129353 476.13803 454.458015 1.081428 3.085555 ... 2.755224 1.116494 3.879258 2.618632 1.114003 1.281729 3.701183 2.016245 1.252894 3.270726
std 78350.449683 31455.117174 21.826895 21.821489 17.898269 268.332958 76.04784 124.236639 0.328495 2.186296 ... 0.810840 0.379688 2.129446 1.953191 0.317817 0.449846 1.125026 0.126415 0.717101 1.215611
min 1.000000 2.000000 0.000000 0.000000 1927.000000 103.000000 127.00000 132.000000 1.000000 0.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 2.000000 1.000000 1.000000
25% 67790.500000 27344.000000 17.000000 17.000000 1988.000000 219.000000 501.00000 501.000000 1.000000 1.000000 ... 3.000000 1.000000 1.000000 1.000000 1.000000 1.000000 4.000000 2.000000 1.000000 3.000000
50% 135765.000000 54590.000000 35.000000 34.000000 2006.000000 416.000000 514.00000 501.000000 1.000000 3.000000 ... 3.000000 1.000000 4.000000 1.000000 1.000000 1.000000 4.000000 2.000000 1.000000 3.000000
75% 203519.250000 81991.000000 52.000000 51.000000 2015.000000 514.000000 514.00000 501.000000 1.000000 5.000000 ... 3.000000 1.000000 6.000000 5.000000 1.000000 2.000000 4.000000 2.000000 1.000000 4.000000
max 271406.000000 109025.000000 104.000000 104.000000 2019.000000 999.000000 514.00000 514.000000 3.000000 9.000000 ... 9.000000 3.000000 6.000000 7.000000 2.000000 2.000000 5.000000 3.000000 9.000000 5.000000

8 rows × 30 columns

from dataprep.eda import plot
plot(df)
DataPrep.EDA Report
Dataset Statistics
Number of Variables 43
Number of Rows 203144
Missing Cells 3.5524e+06
Missing Cells (%) 40.7%
Duplicate Rows 0
Duplicate Rows (%) 0.0%
Total Size in Memory 103.8 MB
Average Row Size in Memory 535.9 B
Variable Types
  • Numerical: 9
  • Categorical: 34
Dataset Insights
ID is uniformly distributed Uniform
AGEA and AGER have similar distributions Similar Distribution
CNAT and CPAYSN have similar distributions Similar Distribution
IDLOG has 3215 (1.58%) missing values Missing
ANNINS has 159062 (78.3%) missing values Missing
APE has 156869 (77.22%) missing values Missing
CNAT has 200732 (98.81%) missing values Missing
COUPLE has 40776 (20.07%) missing values Missing
CPAYSN has 200007 (98.46%) missing values Missing
CPAYSRA has 203013 (99.94%) missing values Missing
Dataset Insights
CS24 has 128604 (63.31%) missing values Missing
CS42 has 128604 (63.31%) missing values Missing
CS8 has 128604 (63.31%) missing values Missing
CSSAL has 140423 (69.12%) missing values Missing
DIPL has 40776 (20.07%) missing values Missing
EMPL has 113506 (55.87%) missing values Missing
EXER has 113506 (55.87%) missing values Missing
IRA has 13748 (6.77%) missing values Missing
MINE has 199871 (98.39%) missing values Missing
PROVRA has 13748 (6.77%) missing values Missing
Dataset Insights
PROVTRA has 113506 (55.87%) missing values Missing
RECH has 144665 (71.21%) missing values Missing
SAL has 191662 (94.35%) missing values Missing
SCOL has 26951 (13.27%) missing values Missing
SECT10 has 113506 (55.87%) missing values Missing
SECT21 has 113506 (55.87%) missing values Missing
SECT5 has 113506 (55.87%) missing values Missing
STAT has 113989 (56.11%) missing values Missing
STATANT has 162112 (79.8%) missing values Missing
TACT has 40776 (20.07%) missing values Missing
Dataset Insights
TP has 113506 (55.87%) missing values Missing
TRAANT has 141812 (69.81%) missing values Missing
TRANS has 40292 (19.83%) missing values Missing
TYPEMPL has 134202 (66.06%) missing values Missing
TYPMENR has 3300 (1.62%) missing values Missing
ANNINS is skewed Skewed
CNAT is skewed Skewed
CPAYSN is skewed Skewed
GAD is skewed Skewed
APE has a high cardinality: 369 distinct values High Cardinality
Dataset Insights
MINE has constant value "1.0" Constant
PROV has constant value "Sud" Constant
APE has constant length 5 Constant Length
CPAYSRA has constant length 5 Constant Length
EMPL has constant length 3 Constant Length
EXER has constant length 3 Constant Length
GENRE has constant length 1 Constant Length
ILN has constant length 1 Constant Length
IRA has constant length 3 Constant Length
MINE has constant length 3 Constant Length
Dataset Insights
NAT has constant length 1 Constant Length
PROV has constant length 3 Constant Length
RECH has constant length 3 Constant Length
SAL has constant length 3 Constant Length
SCOL has constant length 3 Constant Length
SECT10 has constant length 2 Constant Length
SECT21 has constant length 1 Constant Length
SECT5 has constant length 3 Constant Length
STAT has constant length 3 Constant Length
STATANT has constant length 3 Constant Length
Dataset Insights
STM has constant length 1 Constant Length
TACT has constant length 3 Constant Length
TP has constant length 3 Constant Length
TRAANT has constant length 3 Constant Length
TRANS has constant length 3 Constant Length
TYP has constant length 1 Constant Length
TYPEMPL has constant length 3 Constant Length
TYPMENR has constant length 3 Constant Length
GAD has 28705 (14.13%) zeros Zeros
GAQ has 13748 (6.77%) zeros Zeros
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Number of plots per page:

#df2 = df[['ID', 'DIPL', 'EMPL']]
#df2 = df2.fillna(0)
#df2.head()
#df2 = df2.pivot_table('DIPL', 'EMPL', 'ID', aggfunc="sum")

#f, ax = plt.subplots(figsize=(9, 6))
#sns.heatmap(df2, annot=True, linewidths=.5, ax=ax)
df_age = df[['AGER', 'GENRE']]
df_homme = df_age.loc[df_age['GENRE'] == 1].groupby('AGER').sum()
df_homme['AGER'] = df_homme.index
print(df_homme.shape)
print(df_homme.loc[df_homme['AGER'] == 40])
df_homme['GENRE'] = 0-df_homme['GENRE']
df_homme = df_homme.rename(columns={'GENRE': 'homme'})


df_femme = df_age.loc[df_age['GENRE'] == 2].groupby('AGER').sum()
df_femme = df_femme.rename(columns={'GENRE': 'femme'})
df_femme['AGER'] = df_femme.index
print(df_femme.shape)
df_femme.loc[df_femme['AGER'] == 40]
(101, 2)
      GENRE  AGER
AGER             
40     1349    40
(105, 2)
femme AGER
AGER
40 3094 40

Pyramide des ages

sns.set(font_scale = 2)
df4 = pd.concat([df_homme, df_femme], axis=1).iloc[::-1]
figure = plt.figure(figsize=(50, 50))

bar_plot = sns.barplot(x='homme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot = sns.barplot(x='femme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot.set(ylabel="Age", xlabel="Nombre de personnes", title = "Pyramide des âges")
plt.plot([0,0], [0,105], linewidth=2)
#sns;barplot(data=df_age, x=)
[<matplotlib.lines.Line2D at 0x7f38a8ff9f40>]
_images/recensement_11_1.png

Relation entre niveau de diplome, type d’emploi et catégorie socioprofessionnelle

df_metier = df[['DIPL', 'EMPL', 'CS8']].dropna()
sns.set(font_scale = 2)
fig, ax = plt.subplots(figsize=(50, 30))
df_counts = df_metier.groupby(['DIPL', 'EMPL']).size().reset_index()
df_counts.columns.values[df_counts.columns == 0] = 'count'
scale = 500*df_counts['count'].size
size = df_counts['count']/df_counts['count'].sum()*scale
#size = size.astype(float)

#sns.stripplot(x='DIPL', y='EMPL', hue='CS8', data=df_metier, ax=ax) #, size=size, sizes=(10,500)
dipl_id = [1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]
dipl_lbl = ['Pas de scolarisation', 'Aucun diplôme mais scolarisation jusqu’en primaire', 'Aucun diplôme mais scolarisation jusqu’au collège', 
            'Aucun diplôme mais scolarisation au-delà du collège', 'CEP' , 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 'CAP, BEP ou diplôme de niveau équivalent',
            'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
            'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
            'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
            'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
            'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
            'Doctorat de recherche (hors santé)']
#plt.xticks(dipl_id, dipl_lbl, rotation=45, )

empl_lbl = ['Artisan, commerçant, industriel, travailleur indépendant', 'Stagiaire rémunéré, apprenti sous contrat',
            'Salarié du secteur privé à durée déterminée', 'Salarié du secteur privé à durée indéterminée',
            'Salarié du secteur public à durée déterminée', 'Salarié du secteur public à durée indéterminé']


from sklearn.preprocessing import OrdinalEncoder
import numpy as np
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df_metier), columns=list(df_metier.columns))

xnoise, ynoise = np.random.random(len(df_metier))/2, np.random.random(len(df_metier))/2 

sns.scatterplot(enc_df["DIPL"]+xnoise, enc_df["EMPL"]+ynoise, alpha=0.5, hue=enc_df['CS8'], palette="hls")

plt.yticks(np.arange(0.25, len(empl_lbl)+0.25, 1), empl_lbl)

xrange = np.arange(0.25, len(dipl_lbl)+0.25, 1)
plt.xticks(xrange, dipl_lbl, rotation=90)

plt.legend(title='Categories socioprofessionnelles', loc='lower left', labels=['Agriculteurs exploitants', 'Artisans, commerçants et chefs d\'entreprise',
                                                         'Cadres et professions intellectuelles supérieures', 'Professions Intermédiaires',
                                                         'Employés', 'Ouvriers'])
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
<matplotlib.legend.Legend at 0x7f38ade26c40>
_images/recensement_13_2.png